CoditT5 is designed to complement a generation model by providing more
explicit guidance for edits. However, a model that is trained to
generate edits can struggle with coherence and fluency since it is not
actually trained to generate consecutive
text [1]}. By also including the generation
of the target sequence in the pretraining objective, we do mitigate this to some extent, even when there are ambiguities or errors in the edit plan.
However, there appears to be a trade-off between being able to perform the correct edits while maintaining performance with respect to generation metrics.
More specifically, in Tables -, CoditT5 outperforms CodeT5 with respect to xMatch (and GLEU and SARI for comment updating), but underperforms with respect to BLEU-4. To exploit the slight superiority of CodeT5 in this respect, we consider incorporating CodeT5 into our approach.
